For example, when capturing biometric data to be registered in biometric authentication using human palm vein, a position of a hand, which is an example of a capturing target, is guided by a known guiding member or the like. On the other hand, when capturing biometric data to be matched with the registered biometric data, there are cases in which no guide member is provided to guide the position of the hand. When capturing the biometric data of the hand that is not guided by the guide member, a difference between the registered biometric data and the matching biometric data becomes large due to differences in the hand positions, and a success rate of personal identification (or identify verification) may deteriorate. The personal identification is regarded to be successful when a person is correctly authenticated as being that person by the authentication.
An orientation (or attitude) of the hand, such as an inclination of the hand, may be measured, in order to process the biometric data that is captured when matching the captured biometric data with the registered biometric data, so that a deviation of the hand position at the time of the matching from the hand position at the time of the registration is minimized. In this case, the success rate of the personal identification may be improved. The hand position may be detected using a plurality of distance sensors (or range finders), by measuring a distance from each of the distance sensors to a corresponding part of the hand. However, in order to improve the distance measuring accuracy, it is necessary to use a large number of distance sensors, which results in increased cost and increased size of the biometric authentication apparatus. Further, in a case in which there are physical restrictions to the size or the like of the biometric authentication apparatus, it is difficult to arrange the large number of distance sensors within a tolerable range of the physical restrictions.
On the other hand, an SFS (Shape From Shading) technique is known, which recognizes a three-dimensional shape of the capturing target from a luminance distribution of an image of the capturing target captured by irradiating light on the capturing target. When this SFS technique is applied to the detection of the hand position, light is irradiated on the capturing target and reflected light from the capturing target is received by an imaging apparatus via a lens. As a result, the so-called vignetting occurs at the lens. In a case in which the distance from the light source to the capturing target is sufficiently long, the distances from the light source to each of the points on the capturing target may be regarded as being the same, and effects of the vignetting are relatively small. On the other hand, in the case of the biometric authentication apparatus utilizing the palm vein, for example, it is difficult to make the distance from the light source to the hand sufficiently long, due to the above described restrictions or the like, and the effects of the vignetting become relatively large depending on the hand position. For this reason, it is difficult to detect the hand position with a high accuracy using the SFS technique, and improving the matching accuracy between the matching biometric data and the registered biometric data is difficult.
Therefore, according to the conventional shape recognition using the SFS technique, it is difficult to recognize the shape of the capturing target with a high accuracy.
Examples of prior art methods and systems may be found in Japanese Laid-Open Patent Publications No. 2000-230807 and No. 2007-010346, R. Kimmel et al., “Global Shape from Shading”, CVGIP: Image Understanding, pp. 120-125, 1995, R. Zhang et al., “Shape from Shading: A Survey”, IEEE PAMI (Transactions on Pattern Analysis and Machine Intelligence), Vol. 21, No. 8, pp. 690-706, August 1999, E. Prados et al., “Shape from Shading: a well-posed problem?”, INRIA, No. 5297, pp. 1-55, August 2004, and “New Edition Image Analysis Handbook”, University of Tokyo Press, pp. 118-131, September 2004.